472 research outputs found
Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays
Massive multiple-input multiple-output (MIMO) systems, which utilize a large
number of antennas at the base station, are expected to enhance network
throughput by enabling improved multiuser MIMO techniques. To deploy many
antennas in reasonable form factors, base stations are expected to employ
antenna arrays in both horizontal and vertical dimensions, which is known as
full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
planar array (UPA), where antennas are placed in a grid pattern. To exploit the
full benefit of massive MIMO in frequency division duplexing (FDD), the
downlink channel state information (CSI) should be estimated, quantized, and
fed back from the receiver to the transmitter. However, it is difficult to
accurately quantize the channel in a computationally efficient manner due to
the high dimensionality of the massive MIMO channel. In this paper, we develop
both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
of realistic channels and the UPA into consideration. To improve quantization
quality, we focus on not only quantizing dominant radio paths in the channel,
but also combining the quantized beams. We also develop a hierarchical beam
search approach, which scans both vertical and horizontal domains jointly with
moderate computational complexity. Numerical simulations verify that the
performance of the proposed quantizers is better than that of previous CSI
quantization techniques.Comment: 15 pages, 6 figure
Beam Design for Millimeter-Wave Backhaul with Dual-Polarized Uniform Planar Arrays
This paper proposes a beamforming design for millimeter-wave (mmWave)
backhaul systems with dual-polarization antennas in uniform planar arrays
(UPAs). The proposed design method optimizes a beamformer to mimic an ideal
beam pattern, which has flat gain across its coverage, under the dominance of
the line-of-sight (LOS) component in mmWave systems. The dual-polarization
antenna structure is considered as constraints of the optimization. Simulation
results verify that the resulting beamformer has uniform beam pattern and high
minimum gain in the covering region.Comment: To appear in IEEE ICC 202
Soft hydrated sliding interfaces as complex fluids
Hydrogel surfaces are biomimics for sensing and mobility systems in the body such as the eyes and large joints due to their important characteristics of flexibility, permeability, and integrated aqueous component. Recent studies have shown polymer concentration gradients resulting in a less dense region in the top micrometers of the surface. Under shear, this gradient is hypothesized to drive lubrication behavior due to its rheological similarity to a semi-dilute polymer solution. In this work we map 3 lubricating regimes between a polyacrylamide surface and an aluminum annulus using stepped-velocity tribo-rheometry over 5 decades of sliding speed in increasing and decreasing steps. We postulate that the mechanisms of hydrogel-against-hard material lubrication are due to distinct complex fluid behavior characterized by weakly or strongly time-dependent response and thixotropy-like hysteresis. Tribo-rheometry is particularly suited to uncover the lubrication mechanisms of complex interfaces such as are formed with hydrated hydrogel surfaces and biological surfaces
Self-Distilled Self-Supervised Representation Learning
State-of-the-art frameworks in self-supervised learning have recently shown
that fully utilizing transformer-based models can lead to performance boost
compared to conventional CNN models. Striving to maximize the mutual
information of two views of an image, existing works apply a contrastive loss
to the final representations. Motivated by self-distillation in the supervised
regime, we further exploit this by allowing the intermediate representations to
learn from the final layer via the contrastive loss. Through self-distillation,
the intermediate layers are better suited for instance discrimination, making
the performance of an early-exited sub-network not much degraded from that of
the full network. This renders the pretext task easier also for the final
layer, lead to better representations. Our method, Self-Distilled
Self-Supervised Learning (SDSSL), outperforms competitive baselines (SimCLR,
BYOL and MoCo v3) using ViT on various tasks and datasets. In the linear
evaluation and k-NN protocol, SDSSL not only leads to superior performance in
the final layers, but also in most of the lower layers. Furthermore, positive
and negative alignments are used to explain how representations are formed more
effectively. Code will be available.Comment: 15 page
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs
Subgraphs are rich substructures in graphs, and their nodes and edges can be
partially observed in real-world tasks. Under partial observation, existing
node- or subgraph-level message-passing produces suboptimal representations. In
this paper, we formulate a novel task of learning representations of partially
observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax
(PSI) framework and generalize existing InfoMax models, including DGI,
InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the
mutual information between the partial subgraph's summary and various
substructures from nodes to full subgraphs. In addition, we suggest a novel
two-stage model with -hop PSI, which reconstructs the representation of the
full subgraph and improves its expressiveness from different local-global
structures. Under training and evaluation protocols designed for this problem,
we conduct experiments on three real-world datasets and demonstrate that PSI
models outperform baselines.Comment: CIKM 2022 Short Paper (Camera-ready + Appendix
The Impact of CRM on Firm- andRelationship-Level Performance in Distributed Networks
This paper develops and empirically tests a model to evaluate a manufacturer\u27s strategy which provides customer relationship management (CRM) technology to its exclusive retailers. The impact of the strategy on manufacturer-retailer relationship quality is also examined. The research objectives are (1) to identify and test factors that promote active implementation of CRM technology among small retail organizations; (2) to determine whether our expanded concept of CRM implementation that integrates customer information management activities and relationship marketing activities explains CRM performance better; and (3) to investigate whether a manufacturer\u27s support contributes to manufacturer-retailer relationship quality. Statistical analysis shows that the model provides an adequate fit to the data. The retailer\u27s perception of the importance of customer information, manufacturer support, and trade area competitiveness significantly impacts the intensity of CRM implementation by small retailers. CRM implementation intensity positively influences the performance outcomes of CRM, which in turn greatly improves the quality of the manufacturer-retailer relationship. Different from our expectation, supporting retailers with CRM technology did not directly impact the manufacturer-retailer relationship quality. The ease of use of the CRM system also did not influence CRM implementation intensity significantly. The implications of these results and their importance for successful CRM implementation are discussed
Is Signed Message Essential for Graph Neural Networks?
Message-passing Graph Neural Networks (GNNs), which collect information from
adjacent nodes, achieve satisfying results on homophilic graphs. However, their
performances are dismal in heterophilous graphs, and many researchers have
proposed a plethora of schemes to solve this problem. Especially, flipping the
sign of edges is rooted in a strong theoretical foundation, and attains
significant performance enhancements. Nonetheless, previous analyses assume a
binary class scenario and they may suffer from confined applicability. This
paper extends the prior understandings to multi-class scenarios and points out
two drawbacks: (1) the sign of multi-hop neighbors depends on the message
propagation paths and may incur inconsistency, (2) it also increases the
prediction uncertainty (e.g., conflict evidence) which can impede the stability
of the algorithm. Based on the theoretical understanding, we introduce a novel
strategy that is applicable to multi-class graphs. The proposed scheme combines
confidence calibration to secure robustness while reducing uncertainty. We show
the efficacy of our theorem through extensive experiments on six benchmark
graph datasets
Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification
Graph neural networks (GNNs) are commonly used in semi-supervised settings.
Previous research has primarily focused on finding appropriate graph filters
(e.g. aggregation methods) to perform well on both homophilic and heterophilic
graphs. While these methods are effective, they can still suffer from the
sparsity of node features, where the initial data contain few non-zero
elements. This can lead to overfitting in certain dimensions in the first
projection matrix, as training samples may not cover the entire range of graph
filters (hyperplanes). To address this, we propose a novel data augmentation
strategy. Specifically, by flipping both the initial features and hyperplane,
we create additional space for training, which leads to more precise updates of
the learnable parameters and improved robustness for unseen features during
inference. To the best of our knowledge, this is the first attempt to mitigate
the overfitting caused by the initial features. Extensive experiments on
real-world datasets show that our proposed technique increases node
classification accuracy by up to 46.5% relatively
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